Required libraries

The required libraries are loaded - RomicsProcessor written by Geremy Clair (2020) is used to perform trackable transformation and statistics to the dataset - proteinminion written by Geremy Clair (2020) is used to extract fasta information and to perform gene ontology and KEGG pathways enrichement analysis (in prep 2020)

library("RomicsProcessor")
library("proteinminion")
library("eulerr") #for the venn and euler diagrams

Fasta and protein ontologies download using ‘Protein Mini-On’

Using the package ‘Protein Mini-on’ (Geremy Clair 2020, in prep.), The fasta file was downloaded from Uniprot.

if(!file.exists("./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta")){
  download_UniProtFasta(proteomeID = "up000000589", reviewed = F, export=T, file="./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta")
  }

Then we’ve extracted and parsed the details contained in the fasta file header into a table containing a list of details for each protein.

if(!file.exists("./03_Output_files/UniProt_Fasta_info.csv")){
 write.csv(UniprotFastaParser(file = "./03_Output_files/Mus_musculus_proteome_up000000589_2020_07_13.fasta"),file="./03_Output_files/UniProt_Fasta_info.csv")
}

For each entry, ‘Protein Mini-On’ was use to download Gene Ontology (GO) terms and KEGG ids associated with the proteins. This upload was performed the exact same day as the download of the fasta file was done to ensure that the IDs will be identical as the ones present in the fasta file used).

if(file.exists("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv")){
  UniProtTable<-read.csv("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv")
  }else{
  download_UniProtTable(proteomeID = "up000000589", reviewed = F)
  write.csv(UniProtTable,("./03_Output_files/UniprotTable_Mus_musculus_proteome_up000000589_2020_07_13.csv"),row.names=FALSE)
  }

‘Protein-Mini-on’ was then used to generate a table (UniProtTable) containing the list of GOs and their associated protein IDs

if(file.exists("./03_Output_files/UniProtTable_GO.csv")){
  UniProtTable_GO<-read.csv(file="./03_Output_files/UniProtTable_GO.csv")
}else{
generate_UniProtTable_GO()
write.csv(UniProtTable_GO,file="./03_Output_files/UniProtTable_GO.csv",row.names=FALSE)
}

‘Protein-Mini-on’ was used to download similar information from KEGG for the Pathways associated with each protein

if(file.exists("./03_Output_files/UniProtTable_KEGG.csv")){
  UniProtTable_KEGG<-read.csv(file="./03_Output_files/UniProtTable_KEGG.csv")
}else{
generate_UniProtTable_KEGG()
write.csv(UniProtTable_KEGG,file="./03_Output_files/UniProtTable_KEGG.csv",row.names=FALSE)
}

MaxQuant import

The data was searched in MaxQuant using the mouse database generated above using the LFQ quantification and Match Beetwen Runs (MBR) algorithm, the parameter.txt file indicates the parameters employed.

data<-extractMaxQuant("./01_Source_files/proteinGroups.txt",quantification_type = "iBAQ",cont.rm = T,site.rm = T,rev.rm = T)
## [1] "70  Proteins were removed (protein(s) only identified by site,contaminant(s),reverse hit(s))"
## [1] "iBAQ quantification was used"
write.csv(data,file="./03_Output_files/data_raw.csv")
IDsdetails<-extractMaxQuantIDs("./01_Source_files/proteinGroups.txt",cont.rm = T,site.rm = T,rev.rm = T)
## [1] "70  Proteins were removed (protein(s) only identified by site,contaminant(s),reverse hit(s))"
IDsdetails<-cbind(UniProt_Name=sub(".*\\|","",IDsdetails$protein.ids), IDsdetails)
write.csv(IDsdetails,file="./03_Output_files/IDs details.csv")
colnames(data)<- sub("iBAQ.","",colnames(data))
data[,1]<- sub(".*\\|","",data[,1])
metadata<- read.csv(file = "./01_Source_files/metadata.csv")
colnames(metadata)<-tolower(colnames(metadata))

Romics_object creation

The data and metadata were placed in an romics_object, the sample names were retrieved from the metadata, the condition will be use for the coloring of the Figures.

romics_proteins<- romicsCreateObject(data, metadata,main_factor = "Condition")

Data cleaning and normalization

The zeros were replaced by missing values

romics_proteins<-romicsZeroToMissing(romics_proteins)

The proteins to be conserved for quantification were selected to contain at least 60% of complete value for a given condition (2/3 sample of a given condition at least), the overall missingness was evaluated after filtering.

romics_proteins<-romicsFilterMissing(romics_proteins,percentage_completeness = 60)
## [1] "238 rows were removed for the data"
## [1] "Based on the minimum completeness set at 60%"
## [1] "at least the following number of sample(s) containing data was required:"
##  A_G0  A_G1 WT_G0 
##     2     2     2
print(paste0(nrow(romics_proteins$data),"/", nrow(romics_proteins$original_data)," proteins remained after filtering", " (",round(nrow(romics_proteins$data)/nrow(romics_proteins$original_data)*100,2),"%)."))
## [1] "2151/2389 proteins remained after filtering (90.04%)."

The data was log2 transformed, the distriution boxplot were then plotted

romics_proteins<-log2transform(romics_proteins)
distribBoxplot(romics_proteins)

As the same quantity of protein was labelled for each sample, the expectation is that the distribution of the protein abundance is centered, therefore a median centering was performed prior to plot again the distribution boxplots.

romics_proteins<-medianCenterSample(romics_proteins)
distribBoxplot(romics_proteins)

Data imputation

For some of the subsequent statistics imputations are required, we performed an imputation by assuming that the “non-detected” proteins were either low abundance or missing using the method developped by Tyranova et al. (PMID: 27348712). The gray distribution is the data distribution, the yellow distribution is the one for the random values used for imputation.

imputeMissingEval(romics_proteins,nb_stdev = 2,width_stdev = 0.5, bin=1)

romics_proteins<-imputeMissing(romics_proteins,nb_stdev = 2,width_stdev = 0.5)

The PCA grouping were checked after imputation

indPCAplot(romics_proteins, plotType = "percentage")

indPCAplot(romics_proteins, plotType = "individual",Xcomp=1,Ycomp =2,label = F)

indPCAplot(romics_proteins,  plotType = "individual",Xcomp=1,Ycomp =3,label = F)

indPCA3D(romics_proteins)

We will extract the contributions of the proteins to the 3 first components

PCA_results<-romicsPCA(romics_proteins)
PCA_var_coord<-data.frame(PCA_results$var$coord[,1:3])
colnames(PCA_var_coord)<-c("PC1","PC2","PC3")

 ggplot(PCA_var_coord, aes(x=PCA_var_coord[,1], y=PCA_var_coord[,2]))+
    geom_point(size = 3,alpha=I(0.5)) +
    xlab("PC1")+
    ylab("PC2")+
    ggtitle("Principal component analysis protein contributions")+
    theme_ROP()

 ggplot(PCA_var_coord, aes(x=PCA_var_coord[,2], y=PCA_var_coord[,3]))+
    geom_point(size = 3,alpha=I(0.5)) +
    xlab("PC2")+
    ylab("PC3")+
    ggtitle("Principal component analysis protein contributions")+
    theme_ROP()

We’ve extracted the top10% proteins contributing the most to each PCA axis

tenpercentproteins<-round(nrow(PCA_var_coord)*10/100,digits = 0)
 
print("Proteins contributions to PC1")
## [1] "Proteins contributions to PC1"
 top10percentPC1<-PCA_var_coord[1]
 top10percentPC1_names<-rownames(top10percentPC1)
 top10percentPC1<-as.numeric(t(top10percentPC1))
 names(top10percentPC1)<-gsub(";.*","",top10percentPC1_names)
 top10percentPC1<-abs(top10percentPC1)
 top10percentPC1<-top10percentPC1[order(top10percentPC1,decreasing = T)]
 head(data.frame(abs_contrib=top10percentPC1),10)
##            abs_contrib
## Q9QYB1       0.9933345
## Q3TGF2       0.9928248
## Q9CQR2       0.9808388
## P14733       0.9787970
## P49710       0.9780563
## O89053       0.9780260
## P0DOV1       0.9724403
## E9Q4Q2       0.9724311
## Q60591       0.9714436
## A0A5F8MPB5   0.9705373
 top10percentPC1<-names(top10percentPC1[1:tenpercentproteins])
 
print("Proteins contributions to PC2")
## [1] "Proteins contributions to PC2"
 top10percentPC2<-PCA_var_coord[2]
 top10percentPC2_names<-rownames(top10percentPC2)
 top10percentPC2<-as.numeric(t(top10percentPC2))
 names(top10percentPC2)<-gsub(";.*","",top10percentPC2_names)
 top10percentPC2<-abs(top10percentPC2)
 top10percentPC2<-top10percentPC2[order(top10percentPC2,decreasing = T)]
 head(data.frame(abs_contrib=top10percentPC2),10)
##            abs_contrib
## O09159       0.9799565
## Q3TLP8       0.9766476
## P54116       0.9653471
## E9Q616       0.9625872
## G3X8T3       0.9550060
## Q99JI4       0.9526751
## A0A0U1RP68   0.9521342
## P08226       0.9457766
## P10605       0.9391953
## P09528       0.9380859
 top10percentPC2<-names(top10percentPC2[1:tenpercentproteins])
 
print("Proteins contributions to PC3")
## [1] "Proteins contributions to PC3"
 top10percentPC3<-PCA_var_coord[3]
 top10percentPC3_names<-rownames(top10percentPC3)
 top10percentPC3<-as.numeric(t(top10percentPC3))
 names(top10percentPC3)<-gsub(";.*","",top10percentPC3_names)
 top10percentPC3<-abs(top10percentPC3)
 top10percentPC3<-top10percentPC3[order(top10percentPC3,decreasing = T)]
 head(data.frame(abs_contrib=top10percentPC3),10)
##        abs_contrib
## Q91WG2   0.9575724
## P48771   0.9276357
## Q99KP3   0.9206637
## Q6GT24   0.9137083
## Q9CPV4   0.9112642
## B1ATL6   0.9044273
## O70570   0.8900107
## Q78IK2   0.8737668
## Q8VE37   0.8547488
## Q9DBT9   0.8477441
 top10percentPC3<-names(top10percentPC3[1:tenpercentproteins])

 universe<-gsub(";.*","",rownames(PCA_var_coord))
 
  write.csv(top10percentPC1,"./03_Output_files/top10percentPC1.csv")
  write.csv(top10percentPC2,"./03_Output_files/top10percentPC2.csv")
  write.csv(top10percentPC3,"./03_Output_files/top10percentPC3.csv")

Now let’s perform enrichment analysis to evaluate the function participating the most to these separations

PC1_top10_enrich <- cbind(Type="GO top10% PC1", UniProt_GO_Fisher(top10percentPC1,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
PC2_top10_enrich <- cbind(Type="GO top10% PC2", UniProt_GO_Fisher(top10percentPC2,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 214 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
PC3_top10_enrich <- cbind(Type="GO top10% PC3", UniProt_GO_Fisher(top10percentPC3,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
PC1_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC1", UniProt_KEGG_Fisher(top10percentPC1,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
PC2_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC2", UniProt_KEGG_Fisher(top10percentPC2,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 214 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
PC3_top10_enrich_KEGG <- cbind(Type="KEGG top10% PC3", UniProt_KEGG_Fisher(top10percentPC3,universe))
## [1] "Your <query> contained 0 UniProt_IDs and 215 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
enriched_top10_percent<-rbind(PC1_top10_enrich,PC2_top10_enrich,PC3_top10_enrich,PC1_top10_enrich_KEGG ,PC2_top10_enrich_KEGG ,PC3_top10_enrich_KEGG )
enriched_top10_percent<-enriched_top10_percent[enriched_top10_percent$pval<0.05&enriched_top10_percent$fold_change>1,]
head(enriched_top10_percent,10)
##              Type Ontology_type Ontology_accession
## 419 GO top10% PC1            GO               3690
## 294 GO top10% PC1            GO              17124
## 2   GO top10% PC1            GO              30027
## 835 GO top10% PC1            GO              30261
## 838 GO top10% PC1            GO              31936
## 839 GO top10% PC1            GO              45910
## 841 GO top10% PC1            GO              16584
## 611 GO top10% PC1            GO               6355
## 63  GO top10% PC1            GO               5634
## 601 GO top10% PC1            GO               6974
##                            Ongology_description Count_query Count_universe
## 419                double-stranded DNA binding       10/215        19/2150
## 294                         SH3 domain binding       11/215        25/2150
## 2                                lamellipodium       14/215        48/2150
## 835                    chromosome condensation        5/215         6/2150
## 838 negative regulation of chromatin silencing        5/215         6/2150
## 839   negative regulation of DNA recombination        5/215         6/2150
## 841                     nucleosome positioning        5/215         6/2150
## 611 regulation of transcription, DNA-templated       10/215        29/2150
## 63                                     nucleus       64/215       442/2150
## 601   cellular response to DNA damage stimulus        7/215        15/2150
##       %_query %_universe         pval   adjpval fold_change
## 419  4.651163  0.8837209 0.0001331752 0.2291944    5.263158
## 294  5.116279  1.1627907 0.0002105714 0.3623933    4.400000
## 2    6.511628  2.2325581 0.0009562050 1.0000000    2.916667
## 835  2.325581  0.2790698 0.0017322657 1.0000000    8.333333
## 838  2.325581  0.2790698 0.0017322657 1.0000000    8.333333
## 839  2.325581  0.2790698 0.0017322657 1.0000000    8.333333
## 841  2.325581  0.2790698 0.0017322657 1.0000000    8.333333
## 611  4.651163  1.3488372 0.0018712602 1.0000000    3.448276
## 63  29.767442 20.5581395 0.0022403608 1.0000000    1.447964
## 601  3.255814  0.6976744 0.0024141873 1.0000000    4.666667
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Proteins_in_query
## 419                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           IFI5B_MOUSE;PURB_MOUSE;H12_MOUSE;LMNB1_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE;IFIX_MOUSE;IFI4_MOUSE
## 294                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      PTN6_MOUSE;KHDR1_MOUSE;VASP_MOUSE;LYN_MOUSE;HCLS1_MOUSE;ELMO1_MOUSE;CY24A_MOUSE;WASF2_MOUSE;WASP_MOUSE;WIPF1_MOUSE;F8WJB9_MOUSE
## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       BRK1_MOUSE;SHOT1_MOUSE;COR1B_MOUSE;VASP_MOUSE;PDIA1_MOUSE;SNX2_MOUSE;PABP1_MOUSE;HCLS1_MOUSE;ACTB_MOUSE;TWF2_MOUSE;DBNL_MOUSE;ACTA_MOUSE;COR1A_MOUSE;WASF2_MOUSE
## 835                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 838                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 839                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 841                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    H12_MOUSE;H15_MOUSE;H13_MOUSE;H14_MOUSE;H11_MOUSE
## 611                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       SFPQ_MOUSE;H12_MOUSE;H15_MOUSE;LAP2B_MOUSE;H13_MOUSE;H14_MOUSE;HMGA1_MOUSE;H11_MOUSE;Q5SUT0_MOUSE;Z4YKB8_MOUSE
## 63  4EBP1_MOUSE;RGS10_MOUSE;ROA2_MOUSE;SF3A1_MOUSE;MATR3_MOUSE;PTN6_MOUSE;PI42A_MOUSE;PP2AA_MOUSE;LBR_MOUSE;PP1R7_MOUSE;IFI5B_MOUSE;PGAM2_MOUSE;DX39B_MOUSE;KHDR1_MOUSE;PURB_MOUSE;SFPQ_MOUSE;PAK2_MOUSE;RL12_MOUSE;SAMH1_MOUSE;HG2A_MOUSE;KPCB_MOUSE;NFAC2_MOUSE;H12_MOUSE;NOP58_MOUSE;PABP1_MOUSE;FUMH_MOUSE;KCC1A_MOUSE;PSA1_MOUSE;LMNB1_MOUSE;H15_MOUSE;ARPC4_MOUSE;G6PD1_MOUSE;LAP2B_MOUSE;LYN_MOUSE;FYB1_MOUSE;H13_MOUSE;H14_MOUSE;LYRIC_MOUSE;GNAI2_MOUSE;HMGA1_MOUSE;H11_MOUSE;H2B1B_MOUSE;IFIX_MOUSE;HCLS1_MOUSE;H2AX_MOUSE;IFI4_MOUSE;ACTB_MOUSE;DJB11_MOUSE;CY24A_MOUSE;1433B_MOUSE;F120A_MOUSE;CAND1_MOUSE;CLIC4_MOUSE;DNJA2_MOUSE;BASP1_MOUSE;SKP1_MOUSE;TOIP1_MOUSE;TCP4_MOUSE;WASP_MOUSE;Q62347_MOUSE;D3YW09_MOUSE;Q99K94_MOUSE;Q3UJB0_MOUSE;Z4YKB8_MOUSE
## 601                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      SFPQ_MOUSE;SAMH1_MOUSE;NFAC2_MOUSE;FUMH_MOUSE;LYN_MOUSE;H2AX_MOUSE;Q9DCC5_MOUSE

Statistics

The means and stdev are calculated for each group

romics_proteins<-romicsMean(romics_proteins)
## [1] "The Statistics layer was added to your object"
## [1] "Means columns (*_mean) were added to the statistics"
romics_proteins<-romicsSd(romics_proteins)
## [1] "The standard deviation columns (*_sd) were added to the statistics"

Some general statistics are performed (ANOVA, paired T.tests). First the ANOVA was performed

romics_proteins<-romicsANOVA(romics_proteins)
## [1] "The ANOVA columns (ANOVA_p and ANOVA_padj) were added to the statistics"
print(paste0(sum(romics_proteins$statistics$ANOVA_p<0.05), " proteins had an ANOVA p<0.05."))
## [1] "557 proteins had an ANOVA p<0.05."

the pvalue distribtion was plotted

pval<-data.frame(ids=rownames(romics_proteins$statistics), p=romics_proteins$statistics$ANOVA_p)
ggplot(pval, aes(p)) + geom_histogram(binwidth = 0.01)+theme_ROP()+ggtitle("ANOVA p frequency plot")

A heatmap depicting the proteins passing an ANOVA p<0.05 is plotted, the clusters obtained were saved in the statistics.

romicsHeatmap(romics_proteins,variable_hclust_number = 3,ANOVA_filter = "p", p=0.05,sample_hclust = F)

romics_proteins<-romicsVariableHclust(romics_proteins,clusters = 3,ANOVA_filter = "p",p= 0.05,plot = F)
## [1] "The columns hclust_clusters was added to the statistics"
romics_proteins<-romicsZscores(romics_proteins)
## [1] "Z_score_ columns were added to the statistics"

Student’s T.tests were then performed to compare specific conditions with each other.

romics_proteins<-romicsTtest(romics_proteins)
## [1] "T_test columns were added to the statistics"
romicsVolcano(romics_proteins)
## [1] "'stat_type' was missing 't.test' were used by default"

The proteins up and down within each t.test were used to performed enrichment analysis to evaluate what functions were enriched in the different significant groups

Universe<-gsub("\\;.*","",rownames(romics_proteins$statistics))
up_ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(A_G1/A_G0)`>0])
up_WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G0)`>0])
up_WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G1)`>0])
down_ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(A_G1/A_G0)`<0])
down_WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G0)`<0])
down_WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05&romics_proteins$statistics$`log(WT_G0/A_G1)`<0])

up_ASG1_vs_ASG0_GO <- cbind(Enriched_in="up_ASG1_vs_ASG0", UniProt_GO_Fisher(up_ASG1_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 38 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
up_WTG0_vs_ASG0_GO <-cbind(Enriched_in="up_WTG0_vs_ASG0", UniProt_GO_Fisher(up_WTG0_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 263 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
up_WTG0_vs_ASG1_GO <-cbind(Enriched_in="up_WTG0_vs_ASG1", UniProt_GO_Fisher(up_WTG0_vs_ASG1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 352 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_ASG1_vs_ASG0_GO <-cbind(Enriched_in="down_ASG1_vs_ASG0", UniProt_GO_Fisher(down_ASG1_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 180 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_WTG0_vs_ASG0_GO <-cbind(Enriched_in="down_WTG0_vs_ASG0", UniProt_GO_Fisher(down_WTG0_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_WTG0_vs_ASG1_GO <-cbind(Enriched_in="down_WTG0_vs_ASG1", UniProt_GO_Fisher(down_WTG0_vs_ASG1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
up_ASG1_vs_ASG0_KEGG <- cbind(Enriched_in="up_ASG1_vs_ASG0", UniProt_KEGG_Fisher(up_ASG1_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 38 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
up_WTG0_vs_ASG0_KEGG <-cbind(Enriched_in="up_WTG0_vs_ASG0", UniProt_KEGG_Fisher(up_WTG0_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 263 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
up_WTG0_vs_ASG1_KEGG <-cbind(Enriched_in="up_WTG0_vs_ASG1", UniProt_KEGG_Fisher(up_WTG0_vs_ASG1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 352 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_ASG1_vs_ASG0_KEGG <-cbind(Enriched_in="down_ASG1_vs_ASG0", UniProt_KEGG_Fisher(down_ASG1_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 180 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_WTG0_vs_ASG0_KEGG <-cbind(Enriched_in="down_WTG0_vs_ASG0", UniProt_KEGG_Fisher(down_WTG0_vs_ASG0,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
down_WTG0_vs_ASG1_KEGG <-cbind(Enriched_in="down_WTG0_vs_ASG1", UniProt_KEGG_Fisher(down_WTG0_vs_ASG1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 72 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Enrichments_Ttests<-rbind(up_ASG1_vs_ASG0_GO,up_ASG1_vs_ASG0_KEGG,up_WTG0_vs_ASG0_GO,up_WTG0_vs_ASG0_KEGG,up_WTG0_vs_ASG1_GO,up_WTG0_vs_ASG1_KEGG,down_ASG1_vs_ASG0_GO,down_ASG1_vs_ASG0_KEGG,down_WTG0_vs_ASG0_GO,down_WTG0_vs_ASG0_KEGG,down_WTG0_vs_ASG1_GO,down_WTG0_vs_ASG1_KEGG)
  
Enrichments_Ttests <- Enrichments_Ttests[Enrichments_Ttests$pval<0.1& Enrichments_Ttests>1,]

write.csv(up_ASG1_vs_ASG0,file="./03_Output_files/up_ASG1_vs_ASG0.csv")
write.csv(up_WTG0_vs_ASG0,file="./03_Output_files/up_WTG0_vs_ASG0.csv")
write.csv(up_WTG0_vs_ASG1,file="./03_Output_files/up_WTG0_vs_ASG1.csv")
write.csv(down_ASG1_vs_ASG0,file="./03_Output_files/down_ASG1_vs_ASG0.csv")
write.csv(down_WTG0_vs_ASG0,file="./03_Output_files/down_WTG0_vs_ASG0.csv")
write.csv(down_WTG0_vs_ASG1,file="./03_Output_files/down_WTG0_vs_ASG1.csv")

head(Enrichments_Ttests,10)
##         Enriched_in Ontology_type Ontology_accession
## 134 up_ASG1_vs_ASG0            GO              51017
## 280 up_ASG1_vs_ASG0            GO               8092
## 13  up_ASG1_vs_ASG0            GO              42130
## 59  up_ASG1_vs_ASG0            GO               7517
## 169 up_ASG1_vs_ASG0            GO              30388
## 171 up_ASG1_vs_ASG0            GO               6000
## 54  up_ASG1_vs_ASG0            GO               8157
## 32  up_ASG1_vs_ASG0            GO              42802
## 234 up_ASG1_vs_ASG0            GO              51764
## 187 up_ASG1_vs_ASG0            GO               7399
##                             Ongology_description Count_query Count_universe
## 134              actin filament bundle assembly         4/38        15/2150
## 280                cytoskeletal protein binding         3/38        11/2150
## 13  negative regulation of T cell proliferation         2/38         4/2150
## 59                     muscle organ development         2/38         4/2150
## 169 fructose 1,6-bisphosphate metabolic process         2/38         4/2150
## 171                  fructose metabolic process         2/38         4/2150
## 54                protein phosphatase 1 binding         2/38         5/2150
## 32                    identical protein binding        10/38       237/2150
## 234                   actin crosslink formation         2/38         6/2150
## 187                  nervous system development         2/38        10/2150
##       %_query %_universe         pval   adjpval fold_change
## 134 10.526316  0.6976744 0.0002485607 0.0949502   15.087719
## 280  7.894737  0.5116279 0.0015405162 0.5884772   15.430622
## 13   5.263158  0.1860465 0.0042135173 1.0000000   28.289474
## 59   5.263158  0.1860465 0.0042135173 1.0000000   28.289474
## 169  5.263158  0.1860465 0.0042135173 1.0000000   28.289474
## 171  5.263158  0.1860465 0.0042135173 1.0000000   28.289474
## 54   5.263158  0.2325581 0.0058344625 1.0000000   22.631579
## 32  26.315789 11.0232558 0.0074746830 1.0000000    2.387297
## 234  5.263158  0.2790698 0.0076943647 1.0000000   18.859649
## 187  5.263158  0.4651163 0.0173597409 1.0000000   11.315789
##                                                                                                      Proteins_in_query
## 134                                                                   PAWR_MOUSE;DPYL3_MOUSE;A1BN54_MOUSE;Q8VCQ8_MOUSE
## 280                                                                                DESM_MOUSE;ALDOB_MOUSE;E9Q805_MOUSE
## 13                                                                                               UTER_MOUSE;PAWR_MOUSE
## 59                                                                                               LMNA_MOUSE;DESM_MOUSE
## 169                                                                                            F16P1_MOUSE;ALDOB_MOUSE
## 171                                                                                            F16P1_MOUSE;ALDOB_MOUSE
## 54                                                                                               LMNA_MOUSE;PAWR_MOUSE
## 32  RIDA_MOUSE;LMNA_MOUSE;F16P1_MOUSE;GLNA_MOUSE;DPYL3_MOUSE;DHSO_MOUSE;DESM_MOUSE;DPP4_MOUSE;ALDOB_MOUSE;E9Q8N8_MOUSE
## 234                                                                                           DPYL3_MOUSE;A1BN54_MOUSE
## 187                                                                                             NEST_MOUSE;DPYL3_MOUSE

The same was done for the different Clusters of the heatmap

Clust1<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==1])
Clust2<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==2])
Clust3<-gsub("\\;.*","",rownames(romics_proteins$statistics)[!is.na(romics_proteins$statistics$hclust_clusters)&romics_proteins$statistics$hclust_clusters==3])

Clust1_GO<-cbind(Cluster=1, UniProt_GO_Fisher(Clust1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 119 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Clust2_GO<-cbind(Cluster=2, UniProt_GO_Fisher(Clust2,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 341 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Clust3_GO<-cbind(Cluster=3, UniProt_GO_Fisher(Clust3,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 96 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Clust1_KEGG<-cbind(Cluster=1, UniProt_KEGG_Fisher(Clust1,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 119 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Clust2_KEGG<-cbind(Cluster=2, UniProt_KEGG_Fisher(Clust2,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 341 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Clust3_KEGG<-cbind(Cluster=3, UniProt_KEGG_Fisher(Clust3,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 96 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Enrichment_clusters<-rbind(Clust1_GO,Clust2_GO,Clust3_GO,Clust1_KEGG,Clust2_KEGG,Clust3_KEGG)
Enrichment_clusters<-Enrichment_clusters[Enrichment_clusters$pval<0.1 & Enrichment_clusters$fold_change>1,]

write.csv(Clust1,file="./03_Output_files/Clust1.csv")
write.csv(Clust2,file="./03_Output_files/Clust2.csv")
write.csv(Clust3,file="./03_Output_files/Clust3.csv")

head(Enrichment_clusters,10)
##     Cluster Ontology_type Ontology_accession
## 7         1            GO               5615
## 236       1            GO              36094
## 9         1            GO               5764
## 418       1            GO               8199
## 51        1            GO               5509
## 687       1            GO               6826
## 6         1            GO               5576
## 39        1            GO               6879
## 3         1            GO              62023
## 179       1            GO              31232
##                                         Ongology_description Count_query
## 7                                       extracellular space       26/119
## 236                                  small molecule binding        5/119
## 9                                                  lysosome       10/119
## 418                                     ferric iron binding        4/119
## 51                                      calcium ion binding       14/119
## 687                                      iron ion transport        4/119
## 6                                      extracellular region       11/119
## 39                            cellular iron ion homeostasis        5/119
## 3                  collagen-containing extracellular matrix        8/119
## 179 extrinsic component of external side of plasma membrane        3/119
##     Count_universe   %_query %_universe         pval      adjpval fold_change
## 7         120/2150 21.848739  5.5813953 9.060060e-09 9.775804e-06    3.914566
## 236         6/2150  4.201681  0.2790698 1.305574e-04 1.408715e-01   15.056022
## 9          44/2150  8.403361  2.0465116 3.611612e-04 3.896929e-01    4.106188
## 418         4/2150  3.361345  0.1860465 4.269432e-04 4.606718e-01   18.067227
## 51         86/2150 11.764706  4.0000000 5.241995e-04 5.656112e-01    2.941176
## 687         5/2150  3.361345  0.2325581 7.374749e-04 7.957354e-01   14.453782
## 6          59/2150  9.243697  2.7441860 8.018514e-04 8.651977e-01    3.368466
## 39         11/2150  4.201681  0.5116279 9.979355e-04 1.000000e+00    8.212376
## 3          35/2150  6.722689  1.6279070 1.391132e-03 1.000000e+00    4.129652
## 179         3/2150  2.521008  0.1395349 2.501941e-03 1.000000e+00   18.067227
##                                                                                                                                                                                                                                                                                                   Proteins_in_query
## 7   CATB_MOUSE;HEMO_MOUSE;MUG1_MOUSE;PLMN_MOUSE;ALBU_MOUSE;ANXA5_MOUSE;NGP_MOUSE;MUP2_MOUSE;LYZ2_MOUSE;AMBP_MOUSE;APOE_MOUSE;FBN1_MOUSE;DPYL3_MOUSE;A1AT4_MOUSE;ASAH1_MOUSE;CFAD_MOUSE;CLUS_MOUSE;S10A9_MOUSE;TRFE_MOUSE;UROM_MOUSE;VTNC_MOUSE;A0A0R4J0I1_MOUSE;F8WIP8_MOUSE;G3X9T8_MOUSE;Q9CPN9_MOUSE;D3YYD0_MOUSE
## 236                                                                                                                                                                                                                                               MUP20_MOUSE;MUP2_MOUSE;A0A0A6YW77_MOUSE;A2BIN1_MOUSE;Q58EV3_MOUSE
## 9                                                                                                                                                                                              CATB_MOUSE;LMBD1_MOUSE;SAP3_MOUSE;ST1C2_MOUSE;LAMP1_MOUSE;APOE_MOUSE;CAN2_MOUSE;ASAH1_MOUSE;VPS36_MOUSE;Q9JHF5_MOUSE
## 418                                                                                                                                                                                                                                                                   MIOX_MOUSE;FRIH_MOUSE;TRFE_MOUSE;Q9CPX4_MOUSE
## 51                                                                                                                            SCMC1_MOUSE;S10AA_MOUSE;ANXA5_MOUSE;PAMR1_MOUSE;ANXA3_MOUSE;FBN1_MOUSE;CAN2_MOUSE;S10A9_MOUSE;UROM_MOUSE;A0A1B0GR19_MOUSE;A0A494BBD8_MOUSE;A0A1L1SV25_MOUSE;D3YV37_MOUSE;A1BN54_MOUSE
## 687                                                                                                                                                                                                                                                                 FRIH_MOUSE;TRFE_MOUSE;G3X9T8_MOUSE;Q9CPX4_MOUSE
## 6                                                                                                                                                                                       CATB_MOUSE;PLMN_MOUSE;MUP20_MOUSE;ALBU_MOUSE;PAMR1_MOUSE;APOE_MOUSE;FBN1_MOUSE;A1AT4_MOUSE;FRIH_MOUSE;DPP4_MOUSE;TRFE_MOUSE
## 39                                                                                                                                                                                                                                                      HEMO_MOUSE;TRFE_MOUSE;VA0D1_MOUSE;G3X9T8_MOUSE;Q9CPX4_MOUSE
## 3                                                                                                                                                                                                                        CATB_MOUSE;S10AA_MOUSE;PLMN_MOUSE;ANXA5_MOUSE;AMBP_MOUSE;ANXA3_MOUSE;FBN1_MOUSE;VTNC_MOUSE
## 179                                                                                                                                                                                                                                                                                PLMN_MOUSE;APOE_MOUSE;TRFE_MOUSE

Eulerr Diagrams and enrichment

To visualize the proteins that were significant in different comparison a proportional Euler/Venn diagram were plotted the proteins unique to each comparion were used to performed enrichment analysis

#First lets create the lists
ASG1_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$A_G1_vs_A_G0_Ttest_p<0.05])
WTG0_vs_ASG0 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G0_Ttest_p<0.05])
WTG0_vs_ASG1 <- gsub("\\;.*","",rownames(romics_proteins$statistics)[romics_proteins$statistics$WT_G0_vs_A_G1_Ttest_p<0.05])

combined_names<- unique(c(ASG1_vs_ASG0,WTG0_vs_ASG0,WTG0_vs_ASG1))
Venn_table<-data.frame(ASG1_vs_ASG0=(combined_names %in% ASG1_vs_ASG0),WTG0_vs_ASG0=(combined_names %in% WTG0_vs_ASG0),WTG0_vs_ASG1=(combined_names %in% WTG0_vs_ASG1))
rownames(Venn_table)<-combined_names
plot(euler(Venn_table), quantities = TRUE,fills = c("#00646d","#b71e5d","#dba027"))

ASG1_vs_ASG0_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$ASG1_vs_ASG0==1]
WTG0_vs_ASG0_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$WTG0_vs_ASG0==1]
WTG0_vs_ASG1_unique<-rownames(Venn_table)[rowSums(Venn_table)==1 & Venn_table$WTG0_vs_ASG1==1]
shared_in_3_comparisons<-rownames(Venn_table)[rowSums(Venn_table)==3]

ASG1_vs_ASG0_unique_GO<-cbind(Enrichment_for="ASG1_vs_ASG0_unique", UniProt_GO_Fisher(ASG1_vs_ASG0_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 132 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
WTG0_vs_ASG0_unique_GO<-cbind(Enrichment_for="WTG0_vs_ASG0_unique", UniProt_GO_Fisher(WTG0_vs_ASG0_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 123 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
WTG0_vs_ASG1_unique_GO<-cbind(Enrichment_for="WTG0_vs_ASG1_unique", UniProt_GO_Fisher(WTG0_vs_ASG1_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 176 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
ASG1_vs_ASG0_unique_KEGG<-cbind(Enrichment_for="ASG1_vs_ASG0_unique", UniProt_KEGG_Fisher(ASG1_vs_ASG0_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 132 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
WTG0_vs_ASG0_unique_KEGG<-cbind(Enrichment_for="WTG0_vs_ASG0_unique", UniProt_KEGG_Fisher(WTG0_vs_ASG0_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 123 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
WTG0_vs_ASG1_unique_KEGG<-cbind(Enrichment_for="WTG0_vs_ASG1_unique", UniProt_KEGG_Fisher(WTG0_vs_ASG1_unique,Universe))
## [1] "Your <query> contained 0 UniProt_IDs and 176 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your query were converted in Uniprot_IDs."
## [1] "Your universe contained 0 UniProt_IDs and 2150 UniProt_Accession (some might be redundant)."
## [1] "The uniprot_Accession of your universe were converted in Uniprot_IDs."
Venn_unique_enrichments<-rbind(ASG1_vs_ASG0_unique_GO,ASG1_vs_ASG0_unique_KEGG,WTG0_vs_ASG0_unique_GO,WTG0_vs_ASG0_unique_KEGG,WTG0_vs_ASG1_unique_GO,WTG0_vs_ASG1_unique_KEGG)
Venn_unique_enrichments<-Venn_unique_enrichments[Venn_unique_enrichments$pval<0.1&Venn_unique_enrichments$fold_change>1,]
head(Venn_unique_enrichments,10)
##          Enrichment_for Ontology_type Ontology_accession
## 447 ASG1_vs_ASG0_unique            GO               6909
## 423 ASG1_vs_ASG0_unique            GO               3756
## 154 ASG1_vs_ASG0_unique            GO              42130
## 389 ASG1_vs_ASG0_unique            GO              16272
## 625 ASG1_vs_ASG0_unique            GO            1901741
## 126 ASG1_vs_ASG0_unique            GO              43525
## 652 ASG1_vs_ASG0_unique            GO               5834
## 416 ASG1_vs_ASG0_unique            GO              42824
## 422 ASG1_vs_ASG0_unique            GO              15037
## 166 ASG1_vs_ASG0_unique            GO              15629
##                                 Ongology_description Count_query Count_universe
## 447                                    phagocytosis        5/132        14/2150
## 423            protein disulfide isomerase activity        4/132         9/2150
## 154     negative regulation of T cell proliferation        3/132         4/2150
## 389                               prefoldin complex        3/132         4/2150
## 625          positive regulation of myoblast fusion        3/132         4/2150
## 126 positive regulation of neuron apoptotic process        4/132        10/2150
## 652                heterotrimeric G-protein complex        4/132        10/2150
## 416             MHC class I peptide loading complex        3/132         5/2150
## 422       peptide disulfide oxidoreductase activity        3/132         5/2150
## 166                              actin cytoskeleton        7/132        35/2150
##      %_query %_universe        pval adjpval fold_change
## 447 3.787879  0.6511628 0.003608339       1    5.817100
## 423 3.030303  0.4186047 0.005073723       1    7.239057
## 154 2.272727  0.1860465 0.005570421       1   12.215909
## 389 2.272727  0.1860465 0.005570421       1   12.215909
## 625 2.272727  0.1860465 0.005570421       1   12.215909
## 126 3.030303  0.4651163 0.006788911       1    6.515152
## 652 3.030303  0.4651163 0.006788911       1    6.515152
## 416 2.272727  0.2325581 0.008538493       1    9.772727
## 422 2.272727  0.2325581 0.008538493       1    9.772727
## 166 5.303030  1.6279070 0.009064203       1    3.257576
##                                                                   Proteins_in_query
## 447                 RAB5A_MOUSE;NCF2_MOUSE;COR1C_MOUSE;TM9S4_MOUSE;A0A0R4J0I9_MOUSE
## 423                               PDIA3_MOUSE;PDIA1_MOUSE;Q3TML0_MOUSE;E9PXX7_MOUSE
## 154                                                UTER_MOUSE;PAWR_MOUSE;HB2A_MOUSE
## 389                                          PFD2_MOUSE;PFD6_MOUSE;A0A494BBK3_MOUSE
## 625                                               NFAC2_MOUSE;MK14_MOUSE;EHD1_MOUSE
## 126                                    CDC42_MOUSE;ITA1_MOUSE;PAWR_MOUSE;NCF2_MOUSE
## 652                                    GBG5_MOUSE;GNAQ_MOUSE;GNAI2_MOUSE;GBG2_MOUSE
## 416                                             PDIA3_MOUSE;HA11_MOUSE;Q3TCU5_MOUSE
## 422                                            PDIA3_MOUSE;PDIA1_MOUSE;Q3TML0_MOUSE
## 166 WDR1_MOUSE;PLSL_MOUSE;RAB5A_MOUSE;PAWR_MOUSE;FYB1_MOUSE;CSRP1_MOUSE;COR1C_MOUSE
write.csv(ASG1_vs_ASG0_unique,"./03_Output_files/ASG1_vs_ASG0_unique.csv",row.names = F)
write.csv(WTG0_vs_ASG0_unique,"./03_Output_files/WTG0_vs_ASG0_unique.csv",row.names = F)
write.csv(WTG0_vs_ASG1_unique,"./03_Output_files/WTG0_vs_ASG1_unique.csv",row.names = F)

write.table(Venn_unique_enrichments,file="./03_Output_files/Venn_unique_enrichments.txt",sep = "\t",row.names = F)

Data export

The data generated abd the enrichment analysis are exported as a csv file

export_stats<-romicsExportData(romics_proteins,statistics = T,missing_data = T)
export_stats<-cbind(UniProt_Name=rownames(export_stats),export_stats)
export_stats<-merge(export_stats,IDsdetails,by="UniProt_Name")
write.csv(export_stats,file= "./03_Output_files/Cell_cycle_alport_proteomics.csv")
write.table(enriched_top10_percent,file="./03_Output_files/enriched_top10_percent_PC.txt",sep="\t")
write.table(Enrichments_Ttests,file= "./03_Output_files/Enrichments_Ttests.txt",sep="\t")
write.table(Enrichment_clusters,file= "./03_Output_files/Enrichment_clusters.txt",sep="\t")